def plot_noise(kw,current_time):
# store a plot of the psf
outdir = '/tmp/ska_calculator/data/runs/' + current_time + '/'
data = outdir + 'noise.im'
data_fits = outdir + 'noise.fits'
plotname = outdir + 'noise.png'
#public_dir = 'ska_calculator/public/data/' + current_time + '/'
public_dir = '/home/ec2-user/turbogears/ska-calculator/ska_calculator/public/data/' + current_time +'/'
miriad.fits(In=data, out=data_fits, op='xyout')
f=pyfits.open(data_fits)
noise_data = f[0].data
noise_data = noise_data[0,0,:,:]
cdelt = f[0].header['cdelt1']*3600
f.close()
plt.figure(1, figsize=(8,6))
plt.clf() # clear the previous figure
plt.subplot(1,1,1)
midpoint = np.round(len(noise_data)/2)
box_rad = 64
box_min = midpoint-box_rad
box_max = midpoint+box_rad
extent_min = -box_rad * cdelt
extent_max = box_rad * cdelt
extent = [extent_min,extent_max,extent_min,extent_max]
plt.imshow(noise_data[box_min:box_max,box_min:box_max], extent=extent,interpolation='nearest')
plt.colorbar()
plt.xlabel('arcsec', size=10)
plt.ylabel('arcsec', size=10)
plt.title('noise', size=12)
plt.savefig(plotname)
os.system('cp ' + plotname + ' ' + public_dir + '/.')
os.system('cp ' + data_fits + ' ' + public_dir + '/.')
return
def clean(v0, datadir='.'):
curdir = os.getcwd()
try:
os.chdir(datadir)
visdata = glob('./*.uvdata')
if len(visdata) == 0:
raise IOError("could not find %s" % visdata)
elif len(visdata) > 1:
raise IOError("Multiple visibility files found %i" %
(len(visdata)))
visdata = visdata[0]
source, void = os.path.splitext(visdata)
#set C18O rest velocity
void = miriad.puthd(_in=visdata + '/restfreq', value=219.560368)
dv = 0.2
Dv = 4.
nchan = int(Dv / dv)
# v0 = 9.1 #km/s
# cut out all but +/- 2 km/s from rest
# velocity syntax is number of channels, first channel, channel spacing,
# channel width
try:
miriad.uvaver(vis=visdata,
line='velocity,%i,%.1f,%.1f,%.1f' %
(nchan, v0 - Dv / 2, dv, dv),
out=source + '.vcut.uv')
except:
pass
# integrate (average) down to one channel
# will make integrated intensity, but in wrong units
# to get to integrated intensity, multiply by (n*dv)
try:
miriad.uvaver(vis=source + '.vcut.uv',
line='velocity,%i,%.1f,%.1f,%.1f' %
(1, v0 - Dv / 2, Dv, Dv),
out=source + '.aver.uv')
except:
pass
os.system('rm -rf *.dirtymap* *.beam*')
output = miriad.invert(vis=source + '.aver.uv',
map=source + '.dirtymap',
beam=source + '.beam',
cell=0.8,
imsize=100,
robust=1.0,
options='systemp,double')
rms = float(
re.findall("Theoretical rms noise: ([-+]?\d+[\.]?\d*[-+Ee]*\d*)",
output)[0])
#clean the dirty map, just do a very basic clean for now
#restore image
os.system('rm -rf *.clean* *.cleanmap*')
cleanoutput = miriad.clean(map=source + '.dirtymap',
beam=source + '.beam',
out=source + '.clean',
cutoff=rms * 2,
niters=10000,
gain=0.05,
options='positive')
print(cleanoutput)
miriad.restor(map=source + '.dirtymap',
beam=source + '.beam',
model=source + '.clean',
out=source + '.cleanmap')
miriad.fits(_in=source + '.dirtymap',
op='xyout',
out=source + '.dirtymap.fits')
miriad.fits(_in=source + '.beam',
op='xyout',
out=source + '.beam.fits')
miriad.fits(_in=source + '.cleanmap',
op='xyout',
out=source + '.cleanmap.fits')
finally:
os.chdir(curdir)
def subtract(datadir='.', time=0):
curdir = os.getcwd()
try:
os.chdir(datadir)
visdata = glob('*.uvdata')
if len(visdata) == 0:
raise IOError("could not find %s" % visdata)
elif len(visdata) > 1:
raise IOError("Multiple visibility files found %i" %
(len(visdata)))
visdata = visdata[0]
source, void = os.path.splitext(visdata)
cleanext = '.cleanmap' if time == 0 else '.sub%i.cleanmap' % (time - 1)
if not os.path.exists('imfit_sub%i.txt' % time):
os.system('rm sub%i.region' % time)
miriad.cgcurs(_in=source + cleanext,
device='/xs',
options='wedge,cursor,region')
os.system('mv cgcurs.region sub%i.region' % time)
output = miriad.imfit(_in=source + cleanext,
object='gaussian',
region='@sub%i.region' % time)
with open('imfit_sub%i.txt' % time, 'w') as f:
f.write(output)
else:
output = open('imfit_sub%i.txt' % time, 'r').readlines()
output = ''.join(output)
print(output)
imout, rms, beamMajor, beamMinor, beampa = read_imfit(output)
# create zero image
if not os.path.exists(source + '.zero'):
miriad.maths(exp=source + cleanext + '*0', out=source + '.zero')
os.system(
'rm -r *sub%i.model* *sub%i.dirtymap* *sub%i.beam *sub%i.aver.uv' %
(time, time, time, time))
# make model gaussian
miriad.imgen(_in=source + '.zero',
out=source + '.sub%i.model' % time,
object='gaussian',
spar='%f,%f,%f,%f,%f,%f' %
(imout['peak'], imout['x'], imout['y'], imout['dmaj'],
imout['dmin'], imout['dpa']))
#convert to Jy/pixel
factor = 0.8**2 / (2 * np.pi * beamMajor * beamMinor / 2.35482**2)
print(1 / factor)
miriad.maths(exp=source + '.sub%i.model*%f' % (time, factor),
out=source + '.sub%i.model.perpixel' % time)
# convert model gaussian to uv space
uvext = '.aver.uv' if time == 0 else '.sub%i.aver.uv' % (time - 1)
miriad.uvmodel(vis=source + uvext,
model=source + '.sub%i.model.perpixel' % time,
options='subtract',
out=source + '.sub%i.aver.uv' % time)
# create new dirty image with subtracted uv data
miriad.invert(vis=source + '.sub%i.aver.uv' % time,
map=source + '.sub%i.dirtymap' % time,
beam=source + '.sub%i.beam' % time,
cell=0.8,
imsize=100,
robust=1.0,
options='systemp,double')
miriad.fits(_in=source + '.sub%i.dirtymap' % time,
op='xyout',
out=source + '.sub%i.dirtymap.fits' % time)
finally:
os.chdir(curdir)
def plot_psf(kw,current_time):
# store a plot of the psf
outdir = '/tmp/ska_calculator/data/runs/' + current_time + '/'
psf = outdir + 'psf.im'
#public_dir = 'ska_calculator/public/data/' + current_time + '/'
public_dir = '/home/ec2-user/turbogears/ska-calculator/ska_calculator/public/data/' + current_time + '/'
psf_fits = outdir + 'psf.fits'
plotname = outdir + '/psf.png'
if not os.path.exists(public_dir):
os.makedirs(public_dir)
print 'psf=', psf
print 'public_dir=', public_dir
miriad.fits(In=psf, out=psf_fits, op='xyout')
f=pyfits.open(psf_fits)
beam_data = f[0].data
beam_data = beam_data[0,:,:]
#beam_stat = beam_data[960:-960,960:-960]
beam_stat = beam_data
min_range = beam_stat.min()
cdelt = f[0].header['cdelt1']*3600
f.close()
plt.figure(1, figsize=(8,6))
plt.clf() # clear the previous figure
plt.subplot(1,1,1)
midpoint = np.round(len(beam_data)/2)
box_rad = 16
box_min = midpoint-box_rad
box_max = midpoint+box_rad
extent_min = -box_rad * cdelt
extent_max = box_rad * cdelt
#extent = [-64*cdelt,64*cdelt,-64*cdelt,64*cdelt]
#plt.imshow(beam_data[960:-960,960:-960], extent=extent,interpolation='nearest',vmin=min_range,vmax=-3*min_range)
extent = [extent_min,extent_max,extent_min,extent_max]
plt.imshow(beam_data[box_min:box_max,box_min:box_max], extent=extent,interpolation='nearest',vmin=min_range,vmax=-3*min_range)
plt.colorbar()
levels1=[0.1,0.2,0.5]
levels2=[-0.05, -0.01]
plt.contour(beam_data[box_min:box_max,box_min:box_max], levels1, hold='on', colors = 'k',origin='upper', extent=extent, aspect='auto')
plt.contour(beam_data[box_min:box_max,box_min:box_max], levels2, hold='on', colors = 'w',origin='upper', extent=extent, aspect='auto')
plt.xlabel('arcsec', size=10)
plt.ylabel('arcsec', size=10)
plt.title('Beam PSF', size=12)
plt.savefig(plotname)
#print 'BLAAAAAAAAAAAAAAAA$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$'
#print plotname
#print public_dir
#os.system('ls ' + public_dir)
os.system('cp ' + plotname + ' ' + public_dir + '/.')
os.system('cp ' + psf_fits + ' ' + public_dir + '/.')
#os.system('ls -l ' + public_dir)
return
refant = int(line['refant'])
## -- Common data preparation -- ##
for name in names:
# Convert to MIRIAD UVFITS format
in_file = in_path + '/' + obsid + '/' + name + '.fits'
out_file = in_path + '/' + obsid + '/' + name + '.uv'
if not os.path.exists(out_file):
fits_input = {
'In': in_file,
'op': 'uvin',
'out': out_file,
'velocity': 'lsr'
}
miriad.fits(**fits_input)
# Flag auto correlations
in_file = in_path + '/' + obsid + '/' + name + '.uv'
uvflag_input = {'vis': in_file, 'select': 'auto', 'flagval': 'flag'}
miriad.uvflag(**uvflag_input)
# Additional flags
if os.path.exists(in_path + '/' + obsid + '/uvflag.txt'):
flags = ascii.read(in_path + '/' + obsid + '/uvflag.txt',
format='commented_header',
comment='#')
for flag in flags:
uvflag_input = {
'vis': in_file,
'select': str(flag['select']),